translator_engine.py

# 4. translator_engine.py
"""
Moteur de traduction multi-engines avec fallback intelligent
"""
import time
import logging
from typing import List, Optional, Dict, Any
from abc import ABC, abstractmethod
from concurrent.futures import ThreadPoolExecutor, as_completed
import streamlit as st

# Import des différents moteurs
try:
    import translators as ts
except ImportError:
    ts = None

try:
    from googletrans import Translator as GoogleTranslator
except ImportError:
    GoogleTranslator = None

try:
    import deepl
except ImportError:
    deepl = None

try:
    import openai
except ImportError:
    openai = None

try:
    import anthropic
except ImportError:
    anthropic = None

from utils import RateLimiter, TranslationCache

class TranslationEngine(ABC):
    """Classe abstraite pour les moteurs de traduction"""
    
    def __init__(self, name: str):
        self.name = name
        self.logger = logging.getLogger(f"Engine.{name}")
        self.rate_limiter = RateLimiter()
        self.is_available = self.check_availability()
    
    @abstractmethod
    def check_availability(self) -> bool:
        """Vérifie si le moteur est disponible"""
        pass
    
    @abstractmethod
    def translate(self, text: str, source_lang: str, target_lang: str) -> Optional[str]:
        """Traduit le texte"""
        pass
    
    def translate_with_retry(self, text: str, source_lang: str, target_lang: str, max_retries: int = 3) -> Optional[str]:
        """Traduit avec retry automatique"""
        for attempt in range(max_retries):
            try:
                self.rate_limiter.wait()
                result = self.translate(text, source_lang, target_lang)
                if result:
                    self.rate_limiter.reset_errors()
                    return result
            except Exception as e:
                self.logger.warning(f"Tentative {attempt + 1}/{max_retries} échouée: {e}")
                self.rate_limiter.register_error()
                if attempt < max_retries - 1:
                    time.sleep(2 ** attempt)  # Backoff exponentiel
        return None

class TranslatorsEngine(TranslationEngine):
    """Moteur utilisant la bibliothèque translators"""
    
    def __init__(self, provider: str = 'google'):
        self.provider = provider
        super().__init__(f"Translators-{provider}")
    
    def check_availability(self) -> bool:
        return ts is not None
    
    def translate(self, text: str, source_lang: str, target_lang: str) -> Optional[str]:
        if not self.is_available:
            return None
        
        try:
            # Conversion des codes de langue si nécessaire
            if source_lang == 'auto':
                source_lang = 'auto'
            
            result = ts.translate_text(
                text,
                translator=self.provider,
                from_language=source_lang,
                to_language=target_lang,
                timeout=30
            )
            return result
        except Exception as e:
            self.logger.error(f"Erreur traduction {self.provider}: {e}")
            return None

class GoogleTransEngine(TranslationEngine):
    """Moteur Google Translate (googletrans)"""
    
    def __init__(self):
        super().__init__("GoogleTrans")
        self.translator = GoogleTranslator() if GoogleTranslator else None
    
    def check_availability(self) -> bool:
        return GoogleTranslator is not None
    
    def translate(self, text: str, source_lang: str, target_lang: str) -> Optional[str]:
        if not self.is_available:
            return None
        
        try:
            result = self.translator.translate(
                text,
                src=source_lang if source_lang != 'auto' else 'auto',
                dest=target_lang
            )
            return result.text
        except Exception as e:
            self.logger.error(f"Erreur GoogleTrans: {e}")
            return None

class DeepLEngine(TranslationEngine):
    """Moteur DeepL (nécessite une clé API)"""
    
    def __init__(self, api_key: str = None):
        super().__init__("DeepL")
        self.api_key = api_key
        self.translator = None
        if api_key and deepl:
            try:
                self.translator = deepl.Translator(api_key)
            except:
                pass
    
    def check_availability(self) -> bool:
        return self.translator is not None
    
    def translate(self, text: str, source_lang: str, target_lang: str) -> Optional[str]:
        if not self.is_available:
            return None
        
        try:
            # Conversion des codes de langue pour DeepL
            target_lang_deepl = target_lang.upper()
            if target_lang_deepl == 'EN':
                target_lang_deepl = 'EN-US'
            
            result = self.translator.translate_text(
                text,
                source_lang=None if source_lang == 'auto' else source_lang.upper(),
                target_lang=target_lang_deepl
            )
            return result.text
        except Exception as e:
            self.logger.error(f"Erreur DeepL: {e}")
            return None

class OpenAIEngine(TranslationEngine):
    """Moteur OpenAI GPT (nécessite une clé API)"""
    
    def __init__(self, api_key: str = None, model: str = "gpt-3.5-turbo"):
        super().__init__("OpenAI")
        self.api_key = api_key
        self.model = model
        if api_key and openai:
            openai.api_key = api_key
    
    def check_availability(self) -> bool:
        return self.api_key is not None and openai is not None
    
    def translate(self, text: str, source_lang: str, target_lang: str) -> Optional[str]:
        if not self.is_available:
            return None
        
        try:
            # Mapping des codes de langue vers les noms complets
            lang_names = {
                'en': 'English', 'fr': 'French', 'es': 'Spanish',
                'de': 'German', 'it': 'Italian', 'pt': 'Portuguese',
                'ru': 'Russian', 'ja': 'Japanese', 'ko': 'Korean',
                'zh': 'Chinese', 'ar': 'Arabic', 'hi': 'Hindi'
            }
            
            target_name = lang_names.get(target_lang, target_lang)
            
            prompt = f"Translate the following text to {target_name}. Only provide the translation, no explanations:\n\n{text}"
            
            response = openai.ChatCompletion.create(
                model=self.model,
                messages=[
                    {"role": "system", "content": "You are a professional translator. Provide accurate translations while preserving the original meaning and tone."},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.3,
                max_tokens=len(text) * 2  # Estimation généreuse
            )
            
            return response.choices[0].message.content.strip()
        except Exception as e:
            self.logger.error(f"Erreur OpenAI: {e}")
            return None

class AnthropicEngine(TranslationEngine):
    """Moteur Anthropic Claude (nécessite une clé API)"""
    
    def __init__(self, api_key: str = None):
        super().__init__("Anthropic")
        self.api_key = api_key
        self.client = None
        if api_key and anthropic:
            try:
                self.client = anthropic.Anthropic(api_key=api_key)
            except:
                pass
    
    def check_availability(self) -> bool:
        return self.client is not None
    
    def translate(self, text: str, source_lang: str, target_lang: str) -> Optional[str]:
        if not self.is_available:
            return None
        
        try:
            # Mapping des codes de langue
            lang_names = {
                'en': 'English', 'fr': 'French', 'es': 'Spanish',
                'de': 'German', 'it': 'Italian', 'pt': 'Portuguese',
                'ru': 'Russian', 'ja': 'Japanese', 'ko': 'Korean',
                'zh': 'Chinese', 'ar': 'Arabic', 'hi': 'Hindi'
            }
            
            target_name = lang_names.get(target_lang, target_lang)
            
            message = self.client.messages.create(
                model="claude-3-sonnet-20240229",
                max_tokens=len(text) * 2,
                temperature=0.3,
                messages=[
                    {
                        "role": "user",
                        "content": f"Translate this text to {target_name}. Provide only the translation:\n\n{text}"
                    }
                ]
            )
            
            return message.content[0].text
        except Exception as e:
            self.logger.error(f"Erreur Anthropic: {e}")
            return None

class MultiEngineTranslator:
    """Gestionnaire principal avec fallback entre moteurs"""
    
    def __init__(self, config: Dict[str, Any] = None):
        self.config = config or {}
        self.cache = TranslationCache()
        self.logger = logging.getLogger("MultiEngineTranslator")
        self.engines = []
        self._initialize_engines()
    
    def _initialize_engines(self):
        """Initialise tous les moteurs disponibles"""
        
        # Moteurs gratuits (translators)
        for provider in ['google', 'bing', 'yandex', 'baidu']:
            engine = TranslatorsEngine(provider)
            if engine.is_available:
                self.engines.append(engine)
        
        # Google Translate alternatif
        google_engine = GoogleTransEngine()
        if google_engine.is_available:
            self.engines.append(google_engine)
        
        # Moteurs avec API (si les clés sont fournies)
        if self.config.get('deepl_api_key'):
            deepl_engine = DeepLEngine(self.config['deepl_api_key'])
            if deepl_engine.is_available:
                self.engines.insert(0, deepl_engine)  # Priorité haute
        
        if self.config.get('openai_api_key'):
            openai_engine = OpenAIEngine(
                self.config['openai_api_key'],
                self.config.get('openai_model', 'gpt-3.5-turbo')
            )
            if openai_engine.is_available:
                self.engines.insert(0, openai_engine)
        
        if self.config.get('anthropic_api_key'):
            anthropic_engine = AnthropicEngine(self.config['anthropic_api_key'])
            if anthropic_engine.is_available:
                self.engines.insert(0, anthropic_engine)
        
        self.logger.info(f"Moteurs disponibles: {[e.name for e in self.engines]}")
    
    def translate(self, text: str, source_lang: str = 'auto', target_lang: str = 'fr') -> str:
        """
        Traduit le texte avec fallback automatique entre moteurs
        """
        if not text or not text.strip():
            return text
        
        # Vérifier le cache pour chaque moteur
        for engine in self.engines:
            cached = self.cache.get(text, source_lang, target_lang, engine.name)
            if cached:
                self.logger.debug(f"Traduction trouvée en cache ({engine.name})")
                return cached
        
        # Essayer chaque moteur dans l'ordre
        for engine in self.engines:
            self.logger.info(f"Tentative avec {engine.name}")
            try:
                result = engine.translate_with_retry(text, source_lang, target_lang)
                if result:
                    # Sauvegarder en cache
                    self.cache.set(text, result, source_lang, target_lang, engine.name)
                    return result
            except Exception as e:
                self.logger.warning(f"Échec {engine.name}: {e}")
                continue
        
        # Si tous les moteurs échouent, retourner le texte original
        self.logger.error("Tous les moteurs ont échoué, retour du texte original")
        return text
    
    def translate_batch(self, texts: List[str], source_lang: str = 'auto', 
                       target_lang: str = 'fr', max_workers: int = 3) -> List[str]:
        """
        Traduit plusieurs textes en parallèle
        """
        results = [None] * len(texts)
        
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = {
                executor.submit(self.translate, text, source_lang, target_lang): i
                for i, text in enumerate(texts)
            }
            
            for future in as_completed(futures):
                index = futures[future]
                try:
                    results[index] = future.result()
                except Exception as e:
                    self.logger.error(f"Erreur traduction batch index {index}: {e}")
                    results[index] = texts[index]
        
        return results
    
    def get_available_engines(self) -> List[str]:
        """Retourne la liste des moteurs disponibles"""
        return [engine.name for engine in self.engines]
    
    def estimate_cost(self, char_count: int) -> Dict[str, float]:
        """Estime le coût de traduction pour les APIs payantes"""
        costs = {}
        
        # DeepL: ~20€ pour 1M caractères
        if any(e.name == 'DeepL' for e in self.engines):
            costs['DeepL'] = (char_count / 1_000_000) * 20
        
        # OpenAI GPT-3.5: ~$0.002 per 1K tokens (environ 4 caractères par token)
        if any(e.name == 'OpenAI' for e in self.engines):
            token_count = char_count / 4
            costs['OpenAI'] = (token_count / 1000) * 0.002
        
        # Anthropic Claude: ~$0.003 per 1K tokens
        if any(e.name == 'Anthropic' for e in self.engines):
            token_count = char_count / 4
            costs['Anthropic'] = (token_count / 1000) * 0.003
        
        return costs